The paper studies the asymptotic efficiency and robustness of hypothesis tests when models of interest are defined in terms of a weak convergence property. The null and local alternatives induce different limiting distributions for a random element, and a test is considered robust if it controls...

The paper considers time series GMM models where a subset of the parameters are time varying. The magnitude of the time variation in the unstable parameters is such that efficient tests detect the instability with (possibly high) probability smaller than one, even in the limit. We show that for...

The paper investigates asymptotically efficient inference in general likelihood models with time varying parameters. Parameter path estimators and tests of parameter constancy are evaluated by their weighted average risk and weighted average power, respectively. The weight function is...